code: def forward(
      self,
      net: Tensor,
      *,
      apply_tanh: Optional[bool] = None,
  ) -> dict:
      net = self.encode(net)
      out = self.codebook(net, return_z_q_g=True)
      net = self.decode(
          out.z_q, apply_codebook=False, apply_tanh=apply_tanh
      )
      results = {PREDICTIONS_KEY: net}
      results.update(out.to_dict())
      return results
  
imports: import torch
  import torch.nn
  from torch import Tensor
  from typing import Tuple
  from typing import Optional
  from cftool.types import tensor_dict_type
  from .common import IAutoEncoder
  from .common import AutoEncoderInit
  from .common import AutoEncoderLPIPSWithDiscriminator
  from ..general import VQCodebook
  from ...schemas import CustomTrainStepLoss
  from ....schema import IDLModel
  from ....constants import INPUT_KEY
  from ....constants import PREDICTIONS_KEY
  from ....losses.vae import VQVAELoss
label_desc: Use image node_id 4 for calling the AutoEncoderVQ obj's underlying member method code with example usage: obj.forward(net) and returns: results